PSO [1]}, [2]}, [3]} is a biologically inspired metaheuristic algorithm used to search for optimal solutions.
It assigns each data point a particle in the solution space and searches the optimal solution by moving the particles based on simple mathematical formulas.
Each individual particle follows simple local rules but many particles that influence each other create a complex structure.
For our approach, we initialize a swarm of \(N\)  particles in a two-dimensional solution space, where each particle corresponds to a neuron.
With the PSO we aim to optimize two aspects that we encourage with designated update rules.
First, similarly active neurons shall be in the vicinity of each other.
Second, the neuron density shall be consistent in the layout, without gaps or clusters of neurons.
